Corporate Environmental Responsibility: A Study of Single-Use Plastics in Canada
Bibliographic record
Abstract
Globally, millions of people are beginning to acknowledge that a wide-scale environmental crisis is occurring with the deforestation of rainforests, severe weather events, marine plastic pollution, and rising global temperatures and ocean levels. This is prompting many to look at the unsustainable practices of large transnational corporations with their extensive supply chains, high emission rates and excessive packaging. In light of public concern, many of these corporations are making voluntary commitments to become more sustainable as a form of corporate environmental responsibility (CER). This paper explores the motivating factors behind CER through the case study of single-use plastics in Canada to understand whether policies and products are genuine in supporting the environment or a form of greenwashing to deflect government regulations, gain legitimacy in the eyes of the public and increase market share. For the purposes of this research, greenwashing is defined as “the phenomena of socially and environmentally destructive corporations attempting to preserve and expand their markets or power by posing as friends of the environment” (CorpWatch, 2001). Literature reviews are used to provide a brief summary of the history of environmental policy in Canada as well as the history and benefits of single-use plastics, the environmental, human health and economic impacts of plastic pollution, and recent changes in public perceptions. With this background knowledge, case studies of corporate commitments are analyzed to highlight differences between CER and greenwashing and how to discern between the two. In order to provide more insight into the Canadian context, stakeholder interviews were conducted with industry, consulting firms and environmental NGOs. Interviews explored sentiments around single-use plastics, potential motives behind voluntary corporate plastic commitments and areas for improvement in terms of government regulations and corporate practices. This paper concludes with recommendations for corporations and governments on how to more effectively manage CER and plastic pollution, while improving waste management systems in Canada. Areas of focus include extended producer responsibility, material procurement, standardized labelling and content guidelines, and the facilitation of collaboration and innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.040 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".